r Replace only some table values with values from alternate table - r

Question

This is not a "vlookup-and-fill-down" question.
My source data is excellent at delivering all the data I need, just not in in a usable form. Recent changes in volume mean manually adjusted fixes are no longer feasible.
I have an inventory table and a services table. The inventory report does not contain purchase order data for services or non-inventory items. The services table (naturally) does. They are of course different shapes.
Pseudo-coding would be something to the effect of for every inventory$Item in services$Item, replace inventory$onPO with services$onPO.
Sample Data
inv <- structure(list(Item = c("10100200", "10100201", "10100202", "10100203",
"10100204", "10100205-A", "10100206", "10100207", "10100208",
"10100209", "10100210"), onHand = c(600L, NA, 39L, 0L, NA, NA,
40L, 0L, 0L, 0L, 0L), demand = c(3300L, NA, 40L, 40L, NA, NA,
70L, 126L, 10L, 10L, 250L), onPO = c(2700L, NA, 1L, 40L, NA,
NA, 30L, 126L, 10L, 10L, 250L)), .Names = c("Item", "onHand",
"demand", "onPO"), row.names = c(NA, -11L), class = c("data.table",
"data.frame"))
svc <- structure(list(Item = c("10100201", "10100204", "10100205-A"),
`Rcv'd` = c(0L, 0L, 44L), Backordered = c(20L, 100L, 18L)), .Names = c("Item",
"Rcv'd", "Backordered"), row.names = c(NA, -3L), class = c("data.table",
"data.frame"))

Answer 1

Assuming you want to replace NAs in onPO with values from Backordered here is a solution using dplyr::left_join:
library(dplyr);
left_join(inv, svc) %>%
mutate(onPO = ifelse(is.na(onPO), Backordered, onPO)) %>%
select(-Backordered, -`Rcv'd`);
# Item onHand demand onPO
#1 10100200 600 3300 2700
#2 10100201 NA NA 20
#3 10100202 39 40 1
#4 10100203 0 40 40
#5 10100204 NA NA 100
#6 10100205-A NA NA 18
#7 10100206 40 70 30
#8 10100207 0 126 126
#9 10100208 0 10 10
#10 10100209 0 10 10
#11 10100210 0 250 250
Or a solution in base R using merge:
inv$onPO <- with(merge(inv, svc, all.x = TRUE), ifelse(is.na(onPO), Backordered, onPO))
Or using coalesce instead of ifelse (thanks to #thelatemail):
library(dplyr);
left_join(inv, svc) %>%
mutate(onPO = coalesce(onPO, Backordered)) %>%
select(-Backordered, -`Rcv'd`);

Answer 2

In data.table world, this is an "update-join". Join on "Item" and then update the values in the original set with the values from the new set:
library(data.table)
setDT(inv)
setDT(svc)
inv[svc, on="Item", c("onPO","onHand") := .(i.Backordered, `i.Rcv'd`)]
#inv original table
#svc update table
#on= match on specified variable
# := overwrite onPO with Backordered
# onHand with Rcv'd
# Item onHand demand onPO
# 1: 10100200 600 3300 2700
# 2: 10100201 0 NA 20
# 3: 10100202 39 40 1
# 4: 10100203 0 40 40
# 5: 10100204 0 NA 100
# 6: 10100205-A 44 NA 18
# 7: 10100206 40 70 30
# 8: 10100207 0 126 126
# 9: 10100208 0 10 10
#10: 10100209 0 10 10
#11: 10100210 0 250 250

Answer 3

Starting with the tables:
>inv
Item OnHand Demand OnPO
1: 10100200 600 3300 2700
2: 10100201 NA NA NA
3: 10100202 39 40 1
4: 10100203 0 40 40
5: 10100204 NA NA NA
6: 10100205-A NA NA NA
7: 10100206 40 70 30
8: 10100207 0 126 126
9: 10100208 0 10 10
10: 10100209 0 10 10
11: 10100210 0 250 250
> svc
Item Rcv'd Backordered
1: 10100201 0 20
2: 10100204 0 100
3: 10100205-A 44 18
After far more cursing than I'd like to admit, the simple solution that works on the above test data, and my live data proved to be:
# Insert OnHand and OnPO data from svc
for (i in 1:nrow(inv)) {
if(inv$Item[i] %in% svc$Item) {
x <- which(svc$Item == inv$Item[i])
inv$OnPO[i] <- svc$Backordered[x]
inv$OnHand[i] <- svc$`Rcv'd`[x]
}
else{}
}
# cleanup
inv[is.na(inv)] <- 0
Is there a simpler or more obvious method that I've overlooked?

Answer 4

We could use eat from my package safejoin, and "patch"
the matches from the rhs into the lhs when columns conflict.
We rename Backordered to onPO on the way so the two columns conflict as desired.
# devtools::install_github("moodymudskipper/safejoin")
library(safejoin)
library(dplyr)
eat(inv, svc, onPO = Backordered, .conflict = "patch")
# Item onHand demand onPO
# 1 10100200 600 3300 2700
# 2 10100201 NA NA 20
# 3 10100202 39 40 1
# 4 10100203 0 40 40
# 5 10100204 NA NA 100
# 6 10100205-A NA NA 18
# 7 10100206 40 70 30
# 8 10100207 0 126 126
# 9 10100208 0 10 10
# 10 10100209 0 10 10
# 11 10100210 0 250 250